Year: 2024
Type: preprint
Abstract: The Universal Similarity Metric (USM) has been demonstrated to give practically useful measures of "similarity" between sequence data. Here we have used the USM as an alternative distance metric in a K-Nearest Neighbours (K-NN) learner to allow effective pattern recognition of variable length sequence data. We compare this USM approach with the commonly used string-to-word vector approach. Our experiments have used two data sets of divergent domains: (1) spam email filtering and (2) protein subcellular localization. Our results with this data reveal that the USM-based K-NN learner (1) gives predictions with higher classification accuracy than those output by techniques that use the string-to-word vector approach, and (2) can be used to generate reliable probability forecasts.
Source: arXiv (Cornell University)
Authors: Lindsay, David, Lindsay, Sian
Institutions: University of London, Royal Holloway University of London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4396907281